Making large-scale support vector machine learning practical
Advances in kernel methods
Information Hiding Techniques for Steganography and Digital Watermarking
Information Hiding Techniques for Steganography and Digital Watermarking
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Implementation of Key-Based Digital Signal Steganography
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
Steganalysis of JPEG Images: Breaking the F5 Algorithm
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
Reversible hiding in DCT-based compressed images
Information Sciences: an International Journal
Defending against statistical steganalysis
SSYM'01 Proceedings of the 10th conference on USENIX Security Symposium - Volume 10
Image complexity and feature mining for steganalysis of least significant bit matching steganography
Information Sciences: an International Journal
Less detectable JPEG steganography method based on heuristic optimization and BCH syndrome coding
Proceedings of the 11th ACM workshop on Multimedia and security
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A new approach for JPEG resize and image splicing detection
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
Novel stream mining for audio steganalysis
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Improved detection and evaluation for JPEG steganalysis
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Temporal derivative-based spectrum and mel-cepstrum audio steganalysis
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Information Forensics and Security
An improved approach to steganalysis of JPEG images
Information Sciences: an International Journal
A Markov process based approach to effective attacking JPEG steganography
IH'06 Proceedings of the 8th international conference on Information hiding
Modified matrix encoding technique for minimal distortion steganography
IH'06 Proceedings of the 8th international conference on Information hiding
YASS: yet another steganographic scheme that resists blind steganalysis
IH'07 Proceedings of the 9th international conference on Information hiding
Using high-dimensional image models to perform highly undetectable steganography
IH'10 Proceedings of the 12th international conference on Information hiding
Derivative-based audio steganalysis
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A graph–theoretic approach to steganography
CMS'05 Proceedings of the 9th IFIP TC-6 TC-11 international conference on Communications and Multimedia Security
IH'04 Proceedings of the 6th international conference on Information Hiding
Improved detection of LSB steganography in grayscale images
IH'04 Proceedings of the 6th international conference on Information Hiding
How realistic is photorealistic?
IEEE Transactions on Signal Processing
IEEE Transactions on Fuzzy Systems
Spread spectrum image steganography
IEEE Transactions on Image Processing
Steganalysis using image quality metrics
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Derivative-based audio steganalysis
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A method to detect JPEG-based double compression
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Steganalysis of DCT-embedding based adaptive steganography and YASS
Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security
A JPEG-based statistically invisible steganography
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
MiFor '11 Proceedings of the 3rd international ACM workshop on Multimedia in forensics and intelligence
Shift recompression-based feature mining for detecting content-aware scaled forgery in JPEG images
Proceedings of the Twelfth International Workshop on Multimedia Data Mining
Identification of smartphone-image source and manipulation
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Information Sciences: an International Journal
Distortion function designing for JPEG steganography with uncompressed side-image
Proceedings of the first ACM workshop on Information hiding and multimedia security
Steganalysis of F5-like steganography based on selection of joint distribution features
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
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The threat posed by hackers, spies, terrorists, and criminals, etc. using steganography for stealthy communications and other illegal purposes is a serious concern of cyber security. Several steganographic systems that have been developed and made readily available utilize JPEG images as carriers. Due to the popularity of JPEG images on the Internet, effective steganalysis techniques are called for to counter the threat of JPEG steganography. In this article, we propose a new approach based on feature mining on the discrete cosine transform (DCT) domain and machine learning for steganalysis of JPEG images. First, neighboring joint density features on both intra-block and inter-block are extracted from the DCT coefficient array and the absolute array, respectively; then a support vector machine (SVM) is applied to the features for detection. An evolving neural-fuzzy inference system is employed to predict the hiding amount in JPEG steganograms. We also adopt a feature selection method of support vector machine recursive feature elimination to reduce the number of features. Experimental results show that, in detecting several JPEG-based steganographic systems, our method prominently outperforms the well-known Markov-process based approach.